Model-based real-time estimation of center of gravity height using extended Kalman filter

The success of innovative technologies developed in the fields of vehicle safety, energy efficiency, and driving dynamics relies heavily on the accurate modeling of these systems and the precise estimation of critical vehicle dynamic parameters. One of the most influential parameters affecting vehicle behavior during maneuvers is the position of the center of gravity. In particular, the center of gravity height is a key factor determining the vehicle's rollover tendency and plays a direct role in numerous advanced driver assistance systems (ADAS), from active suspension control to stability control systems. Since direct measurement of the center of gravity height is not practically feasible, various algorithms are employed to estimate such parameters. In this thesis, an estimation model is constructed based on the vehicle's roll dynamics, which represent its rollover motion, and the center of gravity height is estimated in real-time using the Extended Kalman Filter (EKF). The developed algorithm is implemented on bus simulation scenarios generated in IPG CarMaker software. Sensor outputs such as suspension forces, vehicle roll acceleration, road bank angle, and lateral tire forces are used as inputs for the estimation. Moreover, the sensitivity of the system is thoroughly analyzed through the Kalman gain and the Jacobian, which is the derivative of the measurement function. This study demonstrates that the vehicle's center of gravity height can be estimated in a model-based and real-time manner solely through sensor data, thus offering a foundational approach for advanced control systems aimed at improving rollover safety.